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Summary of key papers and blogs about diffusion models to learn about the topic. Detailed list of all published diffusion robotics papers.
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# Diffusion-Literature-for-Robotics
> "Creating noise from data is easy; creating data from noise is generative modeling."
Yang Song in "Score-Based Generative Modeling through Stochastic Differential Equations" [Song et al., 2020](https://arxiv.org/pdf/2011.13456)
This repository offers a brief summary of essential papers and blogs on diffusion models, alongside a categorized collection of robotics diffusion papers and useful code repositories for starting your own diffusion robotics project.
---
# Table of Contents
1. [Learning about Diffusion models](#Learning-about-Diffusion-models)
2. [Diffusion in Robotics](#Diffusion-in-Robotics)
2.1 [Imitation Learning and Policy Learning](#Imitation-Learning-and-Policy-Learning)
2.2 [Video Diffusion in Robotics](#Video-Diffusion)
2.3 [Online RL](#Online-RL)
2.4 [Offline RL](#Offline-RL)
2.5 [Inverse RL](#Inverse-RL)
2.6 [World Models](#World-Models)
2.7 [Task and Motion Planning](#tamp)
2.8 [Tactile Sensing & Pose Estimation](#Grasping-&-Tactile-Sensing-&-Pose-Estimation)
2.9 [Robot Design and Development](#Robot-Design-and-Construction)
3. [Code Implementations](#Code-Bases)
4. [Diffusion History](#Diffusion-History)
---
## Learning about Diffusion models
<a name="Learning-about-Diffusion-models"></a>
While there exist many tutorials for Diffusion models, below you can find an overview of some of the best introduction blog posts and video:
- [What are Diffusion Models?](https://www.youtube.com/watch?v=fbLgFrlTnGU&t=1s): an introduction video, which introduces the general idea of diffusion models and some high-level math about how the model works
- [Diffusion Models | Paper Explanation | Math Explained](https://www.youtube.com/watch?v=HoKDTa5jHvg) another great video tutorial explaining the math and notation of diffusion models in detail with visual aid
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://yang-song.net/blog/2021/score/): blog post from the one of the most influential authors in this area, which introduces diffusion models from the score-based perspective
- [What are Diffusion Models](https://lilianweng.github.io/posts/2021-07-11-diffusion-models/): a in-depth blog post about the theory of diffusion models with a general summary on how diffusion model improved over time
- [Understanding Diffusion Models](https://arxiv.org/pdf/2208.11970.pdf): an in-depth explanation paper, which explains the diffusion models from both perspectives with detailed derivations
If you don't like reading blog posts and prefer the original papers, below you can find a list with the most important diffusion theory papers:
- Sohl-Dickstein, Jascha, et al. ["Deep unsupervised learning using nonequilibrium thermodynamics."](http://proceedings.mlr.press/v37/sohl-dickstein15.pdf) _International Conference on Machine Learning_. PMLR, 2015.
- Ho, Jonathan, et al. ["Denoising diffusion probabilistic models."](https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf) _Advances in Neural Information Processing Systems_ 33 (2020): 6840-6851.
- Song, Yang, et al. ["Score-Based Generative Modeling through Stochastic Differential Equations."](https://arxiv.org/pdf/2011.13456) _International Conference on Learning Representations_. 2020.
- Ho, Jonathan, and Tim Salimans. ["Classifier-Free Diffusion Guidance."](https://arxiv.org/pdf/2207.12598) _NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications_. 2021.
- Karras, Tero, et al. ["Elucidating the Design Space of Diffusion-Based Generative Models."](https://arxiv.org/pdf/2206.00364) _Advances in Neural Information Processing Systems_ 35 (2022)
A general list with all published diffusion papers can be found here: [Whats the score?](https://scorebasedgenerativemodeling.github.io/)
---
## Diffusion in Robotics
<a name="Diffusion-in-Robotics"></a>
Since the modern diffusion models have been around for only 3 yeExcerpt of 39,042 characters
Read on GitHub
47
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8b34185170e0de7b, topic:reinforcement-learning, topic:imitation-learning
matched fp:8b34185170e0de7b, topic:diffusion-models
matched fp:8b34185170e0de7b, topic:robotics, name:robotics, desc:robotics